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Impact of Life Adversity and Gene Expression on Psychiatric Symptoms in Children and Adolescents: Findings from the Brazilian High Risk Cohort Study

GSE288870 Homo sapiens (human) RNA-seq (gene expression) 596 samples Submitted 2025/02/18 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 596 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
596 samples: 201 × blood, time point 2, e vs 199 × blood, time point 1, eGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Impact of life adversity and gene expression on psychiatric symptoms in children and adolescents: findings from the Brazilian high risk cohort studyFrontiers in psychiatry · 2025
Summary (written by the submitters)
While the influence of both genetic and environmental factors on the development of psychiatric symptoms is well-recognized, the precise nature of their interaction throughout development remains a subject of ongoing debate. This study investigated the association between the expression of 78 candidate genes, previously associated with psychiatric phenotypes, in peripheral blood and both adversity and psychopathology in a sample of 298 young individuals assessed at two time points from the Brazilian High Risk Cohort Study for Mental Conditions (BHRCS).
Read the full summary ↓
Psychopathology was assessed using the Child Behavior Checklist (CBCL), considering the total CBCL, p-factor (i.e. general factor of psychopathology), and internalizing and externalizing symptoms as clinical variables. The life adversities considered in this study includes four composite variables: child maltreatment, stressful life events, threat and deprivation.Gene expression was measured using next-generation sequencing for target genes and differential gene expression was analyzed with the DESeq2 package. Mixed models revealed six genes associated with internalizing symptoms: NR3C1, HSPBP1, SIN3A, SMAD4, and CRLF3 genes exhibited a negative correlation with these symptoms, while FAR1 gene showed a positive correlation. Additionally, we also found a negative association between USP38 gene expression and externalizing symptoms. Finally, DENND11 and PRRC1 genes were negatively associated with deprivation, a latent factor characterized by neglect, parental absence, and measures of material forms of deprivation. No mediation or moderation effect was observed of gene expression on the association between life adversities and psychiatric symptoms, meaning that they might influence distinct pathways. Among these nine genes, NR3C1, which encodes a glucocorticoid receptor, is by far the most investigated, being associated with depressive symptoms, early life adversity, and stress. While further research is needed to fully understand the complex relationship between gene expression, life adversities, and psychopathology, our findings provide valuable insights into the molecular mechanisms underlying mental disorders.
Published in
Impact of life adversity and gene expression on psychiatric symptoms in children and adolescents: findings from the Brazilian high risk cohort study
Ota VK, Oliveira AM, Bugiga AVG et al. · Frontiers in psychiatry 2025 · PMID 40018685 · doi:10.3389/fpsyt.2025.1505421
This dataset
Open GSE288870 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE288870_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1219655 and SRA study SRP561829. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40018685) ↗
Study design
201 × blood, time point 2, e vs 199 × blood, time point 1, e

Supports a between-group comparison across 400 samples.

How we read this: 2 replicated groups read from the first 400 of 596 sample titles; they account for 400 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 556 more — browse all 596 samples with per-sample file links →

Datasets on a similar topic

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.